Comparison with Gaussian denoisers.   Since we decouple the problem as DMR and RD process, it is interesting to see whether a general denoiser can complete this task. We select several video clips from the testing classes and compare our results with two other denoisers, DnCNN [1]} and FFDNet [2]}. DnCNN is an end-to-end trainable deep CNN for image denoising with different Gaussian noise levels, e.g., [0, 55]. During our testing of DnCNN we found that the pre-trained weights do not perform well. We retrained the network using the Need for Speed dataset with Gaussian noise. The FFDNet is a later variant of DnCNN with the inclusion of pre- and post-processing. During our tuning of the FFDNet, we found that smaller noise levels (a tunable parameter for using the model) result in better denoising performance in terms of PSNR and SSIM metrics. For each testing image, we present the best tuned FFDNet result (noise level less than 10) and compare with our proposed denoiser. The results are summarized in Table REF . Partial resultsFull results can be seen in the supplementary material. with zoom-in figures are presented in Fig. REF .
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